{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "887ea53d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import signal\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "004f3c88",
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = np.random.default_rng()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d8354da",
   "metadata": {},
   "source": [
    "#定义函数detrend\n",
    " \"\"\"\n",
    "    Return x: no detrending.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    x : any object\n",
    "        An object containing the data\n",
    "\n",
    "    axis : int\n",
    "        This parameter is ignored.\n",
    "        It is included for compatibility with detrend_mean\n",
    "\n",
    "    See Also\n",
    "    --------\n",
    "    detrend_mean : Another detrend algorithm.\n",
    "    detrend_linear : Another detrend algorithm.\n",
    "    detrend : A wrapper around all the detrend algorithms.\n",
    "    \"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "430dd594",
   "metadata": {},
   "outputs": [],
   "source": [
    "def detrend_none(x, axis=None):\n",
    "   \n",
    "    return x"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28c1072e",
   "metadata": {},
   "source": [
    "#定义函数M_xcorr\n",
    " r\"\"\"\n",
    "    Plot the cross correlation between *x* and *y*.\n",
    "\n",
    "    The correlation with lag k is defined as\n",
    "    :math:`\\sum_n x[n+k] \\cdot y^*[n]`, where :math:`y^*` is the complex\n",
    "    conjugate of :math:`y`.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    x, y : array-like of length n\n",
    "\n",
    "    maxlags : int, default: 10\n",
    "        Number of lags to show. If None, will return all ``2 * len(x) - 1``\n",
    "        lags.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    lags : array (length ``2*maxlags+1``)\n",
    "        The lag vector.\n",
    "    c : array  (length ``2*maxlags+1``)\n",
    "        The auto correlation vector.\n",
    "    \"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1093f7d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def M_xcorr( x, y, normed=True, detrend=detrend_none,\n",
    "           maxlags=20, **kwargs):\n",
    "   \n",
    "    Nx = len(x)\n",
    "    if Nx != len(y):\n",
    "        raise ValueError('x and y must be equal length')\n",
    "\n",
    "    x = detrend(np.asarray(x))\n",
    "    y = detrend(np.asarray(y))\n",
    "\n",
    "    correls = np.correlate(x, y, mode=\"full\")\n",
    "\n",
    "    if normed:\n",
    "        correls /= np.sqrt(np.dot(x, x) * np.dot(y, y))\n",
    "\n",
    "    if maxlags is None:\n",
    "        maxlags = Nx - 1\n",
    "\n",
    "    if maxlags >= Nx or maxlags < 1:\n",
    "        raise ValueError('maxlags must be None or strictly '\n",
    "                         'positive < %d' % Nx)\n",
    "\n",
    "    lags = np.arange(-maxlags, maxlags + 1)\n",
    "    correls = correls[Nx - 1 - maxlags:Nx + maxlags]\n",
    "\n",
    "\n",
    "    return  correls, lags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "db3d0f83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "h = 1/3*np.array([1,1,1])\n",
    "x = np.random.randn(1000,1)\n",
    "\n",
    "y = signal.lfilter(h,1,x)\n",
    "x = np.array(x).flatten()\n",
    "\n",
    "y = np.array(y).flatten()\n",
    "[xc,lags] = M_xcorr(y, y ,20)\n",
    "\n",
    "Xc = np.zeros(np.size(xc))\n",
    "\n",
    "Xc[18:23] = np.array([1, 2, 3, 2, 1])/9*np.var(x)\n",
    "figure = plt.figure(111)\n",
    "plt.stem(lags,xc,label = '$Sample autocorrelation$')\n",
    "\n",
    "markerline, stemlines, baseline = plt.stem(lags,Xc,linefmt = 'r-',label = '$Theoretical autocorrelation$')\n",
    "plt.setp(stemlines, 'linewidth', 2)\n",
    "plt.legend(loc = \"upper right\")\n",
    "\n",
    "figure = plt.figure(211)\n",
    "wx, pxx= signal.welch(x)\n",
    "wy, pyy = signal.welch(y)\n",
    "plt.plot(wx/np.pi,20*np.log10(pxx),label = '$Original sequence$')\n",
    "plt.plot(wy/np.pi,20*np.log10(pyy),label = '$Filtered sequence$')\n",
    "plt.legend(loc = \"lower left\")\n",
    "\n",
    "plt.xlabel('$Normalized Frequency (\\times\\pi rad/sample)$')\n",
    "plt.ylabel('$Power/frequency (dB/rad/sample)$')\n",
    "plt.title('$Welch Power Spectral Density Estimate$')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ced9c131",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
